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README.md
__init__.py
__main__.py
model.py

README.md

DecAtt

This is a PyTorch reimplementation of the following paper:

@inproceedings{parikh-EtAl:2016:EMNLP2016,
  author     = {Parikh, Ankur  and  T\"{a}ckstr\"{o}m, Oscar  and  Das, Dipanjan  and  Uszkoreit, Jakob},
  title    = {A Decomposable Attention Model for Natural Language Inference},
booktitle  = {Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year     = {2016}
} 

Please ensure you have followed instructions in the main README doc before running any further commands in this doc. The commands in this doc assume you are under the root directory of the Castor repo.

SICK Dataset

To run DecAtt on the SICK dataset, use the following command. --dropout 0 is for mimicking the original paper, although adding dropout can improve results. If you have any problems running it check the Troubleshooting section below.

python -m decatt decatt.sick.model --dataset sick --epochs 500 --regularization 5e-4 --lr 0.001 --lr-reduce-factor 0.5 --dropout 0.1
Implementation and config Pearson's r Spearman's p MSE
PyTorch using above config 0.80094564 0.7184082390455326 0.3711671233177185

TrecQA Dataset

To run DecAtt on the TrecQA dataset, use the following command:

python -m decatt decatt.trecqa.model --dataset trecqa --epochs 500 --regularization 5e-4 --lr 0.001 --lr-reduce-factor 0.5 --dropout 0.1
Implementation and config map mrr
PyTorch using above config 0.6536 0.6848

This are the TrecQA raw dataset results. The paper results are reported in Noise-Contrastive Estimation for Answer Selection with Deep Neural Networks.

WikiQA Dataset

You also need trec_eval for this dataset, similar to TrecQA.

Then, you can run:

python -m decatt decatt.wikiqa.model --dataset wikiqa --epochs 500 --regularization 5e-4 --lr 0.001 --lr-reduce-factor 0.5 --dropout 0.1
Implementation and config map mrr
PyTorch using above config 0.6462 0.6603

To see all options available, use

python -m decatt --help

Optional Dependencies

To optionally visualize the learning curve during training, we make use of https://github.com/lanpa/tensorboard-pytorch to connect to TensorBoard. These projects require TensorFlow as a dependency, so you need to install TensorFlow before running the commands below. After these are installed, just add --tensorboard when running the training commands and open TensorBoard in the browser.

pip install tensorboardX
pip install tensorflow-tensorboard
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